imagenette.py

import os
from pathlib import Path

import datasets


_CITATION = """
@misc{imagenette,
  author    = "Jeremy Howard",
  title     = "imagenette",
  url       = "https://github.com/fastai/imagenette/"
}
"""

_DESCRIPTION = """\
Imagenette is a subset of 10 easily classified classes from the Imagenet
dataset. It was originally prepared by Jeremy Howard of FastAI. The objective
behind putting together a small version of the Imagenet dataset was mainly
because running new ideas/algorithms/experiments on the whole Imagenet take a
lot of time.
This version of the dataset allows researchers/practitioners to quickly try out
ideas and share with others. The dataset comes in three variants:
  * Full size
  * 320 px
  * 160 px
Note: The v2 config correspond to the new 70/30 train/valid split (released
in Dec 6 2019).
"""

_LABELS_FNAME = "image_classification/imagenette_labels.txt"
_URL_PREFIX = "https://s3.amazonaws.com/fast-ai-imageclas/"

LABELS = [
    "n01440764",
    "n02102040",
    "n02979186",
    "n03000684",
    "n03028079",
    "n03394916",
    "n03417042",
    "n03425413",
    "n03445777",
    "n03888257"
]

class ImagenetteConfig(datasets.BuilderConfig):
  """BuilderConfig for Imagenette."""

  def __init__(self, size, base, **kwargs):
    super(ImagenetteConfig, self).__init__(
        # `320px-v2`,...
        name=size + ("-v2" if base == "imagenette2" else ""),
        description="{} variant.".format(size),
        **kwargs)
    # e.g. `imagenette2-320.tgz`
    self.dirname = base + {
        "full-size": "",
        "320px": "-320",
        "160px": "-160",
    }[size]


def _make_builder_configs():
  configs = []
  for base in ["imagenette2", "imagenette"]:
    for size in ["full-size", "320px", "160px"]:
      configs.append(ImagenetteConfig(base=base, size=size))
  return configs


class Imagenette(datasets.GeneratorBasedBuilder):
  """A smaller subset of 10 easily classified classes from Imagenet."""

  VERSION = datasets.Version("1.0.0")

  BUILDER_CONFIGS = _make_builder_configs()

  def _info(self):
    return datasets.DatasetInfo(
        # builder=self,
        description=_DESCRIPTION,
        features=datasets.Features({
            "image_file_path": datasets.Value("string"),
            "labels": datasets.ClassLabel(names=LABELS)
        }),
        supervised_keys=("image_file_path", "labels"),
        homepage="https://github.com/fastai/imagenette",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    print(self.__dict__.keys())
    print(self.config)
    dirname = self.config.dirname
    url = _URL_PREFIX + "{}.tgz".format(dirname)
    path = dl_manager.download_and_extract(url)
    train_path = os.path.join(path, dirname, "train")
    val_path = os.path.join(path, dirname, "val")
    assert os.path.exists(train_path)
    return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "datapath": train_path,
            },
        ),
        datasets.SplitGenerator(
            name=datasets.Split.VALIDATION,
            gen_kwargs={
                "datapath": val_path,
            },
        ),
    ]

  def _generate_examples(self, datapath):
    """Yields examples."""
    for path in Path(datapath).glob("**/*.JPEG"):
        record = {
            "image_file_path": str(path),
            "labels": path.parent.name
        }
        yield path.name, record